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A decision support tool for implementing machine learning in SME manufacturing companies
Thesis (MEM)--Stellenbosch University, 2024.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2024
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| _version_ | 1867613740266946560 |
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| access_status_str | Open Access |
| author | Van Coller, Christiaan |
| author2 | Louw, Louis |
| author_browse | Louw, Louis Van Coller, Christiaan |
| author_facet | Louw, Louis Van Coller, Christiaan |
| author_sort | Van Coller, Christiaan |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MEM)--Stellenbosch University, 2024. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/130626 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:40:56.936Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/130626 A decision support tool for implementing machine learning in SME manufacturing companies Van Coller, Christiaan Louw, Louis Palm, Daniel Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. Engineering Management (MEM). Decision support systems Manufacturing industries Machine leanrining Small business UCTD Thesis (MEM)--Stellenbosch University, 2024. ENGLISH ABSTRACT: The manufacturing industry is an integral part of a country’s economy. Having a strong manufacturing industry, and especially strong small and medium-sized enterprises (SMEs) in manufacturing, brings a significant benefit to countries and their people. A tool that can be used for improving manufacturing companies is machine learning. Machine learning can be used to significantly improve efficiency, traceability, reduce costs, as well as many other benefits. There are, however, many limitations, especially for SMEs, that can withhold them from unlocking the full potential of machine learning. This thesis aims to provide SMEs with the necessary tools to implement machine learning into their manufacturing operations. This includes the necessary requirements, benefits, and drawbacks to implementing machine learning. This aim is achieved through a literature review identifying the needs, requirements, and benefits of implementing machine learning in manufacturing, as well as the use of a decision support tool for implementing machine learning into the manufacturing operations of SMEs. This thesis focuses specifically on the manufacturing industry; therefore, all context falls within the manufacturing industry. Although the tool might have limited applications outside of manufacturing, it is designed for the manufacturing industry. The tool generates a project plan, guidelines for data preparation, the most applicable algorithm for the scenario as well as template code, evaluation guidelines, and deployment guidelines. These are generated using information provided by the user of the tool that is processed by OpenAI’s GPT-4 large language model. These outputs are validated using dataset tests, interviews, and a case study. The validation process showed that the tool is accurate and reliable, relevant in its recommendations, user friendly in terms of its user interface, and that it provides accurate and practical code for the appropriate algorithms. The thesis as a whole highlights the unique challenges and limitations faced by SME manufacturing companies, after which it addresses the challenges associated with machine learning. For further research, improvements can be made to the decision support tool to make it a direct channel for machine learning implementation. Having a functionality that allows users to upload their data will significantly improve the guidance of the tool as well as the algorithm that will be suggested. AFRIKAANSE OPSOMMING: Die vervaardigingsindustrie is 'n integrale deel van 'n land se ekonomie. Om 'n sterk vervaardigingsindustrie te hê, en veral sterk klein en mediumgrootte ondernemings (KMO's) in vervaardiging, bring aansienlike voordele vir lande en hul mense. Masjienleer kan gebruik word om aansienlike verbeteringe te bring aan vervaardigingsmaatskappye. Masjienleer kan gebruik word om doeltreffendheid aansienlik te verbeter, naspeurbaarheid te verhoog, kostes te verminder, asook vele ander voordele. Daar is egter baie beperkings, veral vir KMO's, wat hulle kan weerhou van die volle benutting van masjienleer se potensiaal. Hierdie tesis is geskryf met die doel om KMO's van die nodige gereedskap te voorsien om masjienleer in hul vervaardigingsbedrywighede te implementeer. Dit sluit die nodige vereistes, voordele en nadele van die implementering van masjienleer in. Hierdie doel word bereik deur ‘n literatuur studie wat die behoeftes, vereistes en voordele van die implimentering van masjienleer in vervaardiging identifiseer, asook deur die die gebruik van 'n besluitsteuninstrument vir die implementering en operasionalisering van masjienleer in die vervaardigingsbedrywighede van KMO's. Hierdie tesis fokus spesifiek op die vervaardigingsindustrie; dus val alle kontekste binne die vervaardigingsindustrie. Alhoewel die instrument beperkte toepassings buite vervaardiging mag hê, is dit ontwerp vir die vervaardigingsindustrie. Die instrument genereer 'n projekplan, riglyne vir data voorbereiding, die mees toepaslike algoritme vir die scenario sowel as templaatkode, evalueringsriglyne, en implementeringsriglyne. Hierdie word gegenereer deur gebruik te maak van inligting wat deur die gebruiker van die instrument verskaf word en wat deur OpenAI se GPT-4 groot taalmodel verwerk word. Hierdie uitsette word gevalideer deur middel van datasteltoetse, onderhoude en 'n gevallestudie. Die valideringsproses het getoon dat die instrument akkuraat en betroubaar is in sy aanbevelings, gebruikersvriendelik in terme van sy gebruikersoppelvlak, en dat dit akkurate en praktiese kode bied vir die toepaslike algoritmes. Die proefskrif as geheel beklemtoon die unieke uitdagings en beperkinge wat deur KMO-vervaardigingsmaatskappye in die gesig gestaar word, waarna dit die uitdagings aanspreek wat met masjienleer geassosieer word. Vir verdere navorsing kan verbeterings aan die besluitsteuninstrument aangebring word om dit 'n direkte kanaal te maak vir die implementering van masjienleer. 'n Funksionaliteit wat gebruikers toelaat om hul data op te laai, sal die leiding van die instrument sowel as die voorgestelde algoritme aansienlik verbeter. 2024-02-11T07:45:44Z 2024-04-27T00:27:39Z 2024-02-11T07:45:44Z 2024-04-27T00:27:39Z 2024-03 Thesis https://scholar.sun.ac.za/handle/10019.1/130626 en_ZA Stellenbosch University xi, 159 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Decision support systems Manufacturing industries Machine leanrining Small business UCTD Van Coller, Christiaan A decision support tool for implementing machine learning in SME manufacturing companies |
| title | A decision support tool for implementing machine learning in SME manufacturing companies |
| title_full | A decision support tool for implementing machine learning in SME manufacturing companies |
| title_fullStr | A decision support tool for implementing machine learning in SME manufacturing companies |
| title_full_unstemmed | A decision support tool for implementing machine learning in SME manufacturing companies |
| title_short | A decision support tool for implementing machine learning in SME manufacturing companies |
| title_sort | decision support tool for implementing machine learning in sme manufacturing companies |
| topic | Decision support systems Manufacturing industries Machine leanrining Small business UCTD |
| url | https://scholar.sun.ac.za/handle/10019.1/130626 |
| work_keys_str_mv | AT vancollerchristiaan adecisionsupporttoolforimplementingmachinelearninginsmemanufacturingcompanies AT vancollerchristiaan decisionsupporttoolforimplementingmachinelearninginsmemanufacturingcompanies |